In Silico Design of Covalent Organic Framework-Based Electrocatalysts.

Abstract

Covalent organic frameworks (COFs) are an emerging type of porous crystalline material for efficient catalysis of the oxygen evolution reaction (OER). However, it remains a grand challenge to address the best candidates from thousands of possible COFs. Here, we report a methodology for the design of the best candidate screened from 100 virtual M–NxOy (M = 3d transition metal)-based model catalysts via density functional theory (DFT) and machine learning (ML). The intrinsic descriptors of OER activity of M–NxOy were addressed by the machine learning and used for predicting the best structure with OER performances. One of the predicted structures with a Ni–N2O2 unit is subsequently employed to synthesize the corresponding Ni–COF. X-ray absorption spectra characterizations, including XANES and EXAFS, validate the successful synthesis of the Ni–N2O2 coordination environment. The studies of electrocatalytic activities confirm that Ni–COF is comparable with the best reported COF-based OER catalysts. The current density reaches 10 mA cm–2 at a low overpotential of 335 mV. Furthermore, Ni–COF is stable for over 65 h during electrochemical testing. This work provides an accelerating strategy for the design of new porous crystalline-material-based electrocatalysts.